fitVsDatCorrelation=0.791361723271223 cont.fitVsDatCorrelation=0.239888831793892 fstatistic=8449.34960801038,53,715 cont.fstatistic=3342.59909184992,53,715 residuals=-0.549664875612731,-0.0891633000705181,-0.00733621351074939,0.076789653479553,0.841042286101451 cont.residuals=-0.479173401279922,-0.174936669025462,-0.0570493547364991,0.115478378760729,1.54174757940179 predictedValues: Include Exclude Both Lung 51.6287525187594 51.8235075505715 57.6570116306622 cerebhem 61.5175047327588 73.5669964306249 85.0224857894827 cortex 48.1963266106283 57.6202045404181 67.2752739584646 heart 50.0428562071927 51.5689752671541 52.6157526499425 kidney 49.8342586753422 50.8499498263776 60.2848288123463 liver 50.4477188522961 51.704086598569 55.2568379863425 stomach 49.7915833809423 54.3524994469881 58.6041765496232 testicle 51.4058364735911 58.2891148526838 52.3474696026168 diffExp=-0.194755031812093,-12.0494916978661,-9.42387792978978,-1.52611905996134,-1.01569115103538,-1.25636774627289,-4.56091606604576,-6.8832783790927 diffExpScore=0.973622081547287 diffExp1.5=0,0,0,0,0,0,0,0 diffExp1.5Score=0 diffExp1.4=0,0,0,0,0,0,0,0 diffExp1.4Score=0 diffExp1.3=0,0,0,0,0,0,0,0 diffExp1.3Score=0 diffExp1.2=0,0,0,0,0,0,0,0 diffExp1.2Score=0 cont.predictedValues: Include Exclude Both Lung 52.288856089868 57.9498317618519 63.0051845852711 cerebhem 52.3039510424944 58.262257510802 52.3618642125797 cortex 52.6366659020871 59.4897688163552 49.9507637608815 heart 52.6836815261882 50.0798705947965 56.1638119700546 kidney 53.9787638304905 58.7526983563234 53.6904070433864 liver 51.3638759508978 54.8559526494828 53.1558386637845 stomach 54.3457722482275 61.541582327255 51.145371555844 testicle 52.4257372141098 63.0409774359091 56.5759744207013 cont.diffExp=-5.66097567198385,-5.95830646830758,-6.85310291426804,2.60381093139169,-4.77393452583284,-3.49207669858500,-7.19581007902746,-10.6152402217993 cont.diffExpScore=1.09797554045376 cont.diffExp1.5=0,0,0,0,0,0,0,0 cont.diffExp1.5Score=0 cont.diffExp1.4=0,0,0,0,0,0,0,0 cont.diffExp1.4Score=0 cont.diffExp1.3=0,0,0,0,0,0,0,0 cont.diffExp1.3Score=0 cont.diffExp1.2=0,0,0,0,0,0,0,-1 cont.diffExp1.2Score=0.5 tran.correlation=0.875226375416953 cont.tran.correlation=0.35186439675614 tran.covariance=0.00778839125303765 cont.tran.covariance=0.000447358170086891 tran.mean=53.9150107478061 cont.tran.mean=55.3750152035712 weightedLogRatios: wLogRatio Lung -0.0148570311665169 cerebhem -0.752840708685328 cortex -0.70803586682219 heart -0.117995980560868 kidney -0.0790673399561714 liver -0.0967548540646887 stomach -0.346342540570969 testicle -0.502979259735563 cont.weightedLogRatios: wLogRatio Lung -0.412018486096519 cerebhem -0.432718476091312 cortex -0.492577477865385 heart 0.199652678444197 kidney -0.341609576254978 liver -0.261248971510888 stomach -0.504539912676926 testicle -0.747062026081635 varWeightedLogRatios=0.0876385409720824 cont.varWeightedLogRatios=0.074001307810881 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.90605401783598 0.0843371697761561 46.3147391381908 1.98365215480402e-217 *** df.mm.trans1 -0.0735833707840544 0.074893038962592 -0.982512818324922 0.326179608288053 df.mm.trans2 0.042066204357427 0.0681249580604963 0.617485948689633 0.537110846873177 df.mm.exp2 0.137191777758317 0.0918425670316836 1.49377115854121 0.135676593502766 df.mm.exp3 -0.117047415854696 0.0918425670316836 -1.27443536954185 0.202923199495393 df.mm.exp4 0.0553737114014499 0.0918425670316836 0.60291990077267 0.54675316096571 df.mm.exp5 -0.098909401657294 0.0918425670316836 -1.07694509043037 0.28186802160251 df.mm.exp6 0.0170715221662399 0.0918425670316836 0.185878103345594 0.852593073712021 df.mm.exp7 -0.00488013764700628 0.0918425670316836 -0.0531359020629589 0.957638468300944 df.mm.exp8 0.209852749539696 0.0918425670316836 2.28491816291787 0.0226096648435398 * df.mm.trans1:exp2 0.0380512497014731 0.0872088262136492 0.436323378648084 0.662733837953045 df.mm.trans2:exp2 0.213160869304174 0.073436403582585 2.90265942918157 0.00381397042317578 ** df.mm.trans1:exp3 0.0482514859550011 0.0872088262136493 0.553286726240206 0.58024004499192 df.mm.trans2:exp3 0.223076835280396 0.073436403582585 3.03768736481667 0.00247078098345788 ** df.mm.trans1:exp4 -0.0865726856048458 0.0872088262136493 -0.992705547862266 0.321189296685685 df.mm.trans2:exp4 -0.0602973342931621 0.073436403582585 -0.821082342701506 0.411873042404517 df.mm.trans1:exp5 0.0635333377292324 0.0872088262136492 0.728519583253932 0.466534118723256 df.mm.trans2:exp5 0.0799446774120723 0.073436403582585 1.08862462637033 0.276686246891516 df.mm.trans1:exp6 -0.0402127290531463 0.0872088262136493 -0.461108477192788 0.644861010171022 df.mm.trans2:exp6 -0.0193785593566892 0.073436403582585 -0.263882194814953 0.791946731815358 df.mm.trans1:exp7 -0.0313526376749923 0.0872088262136493 -0.359512208066908 0.71931809844645 df.mm.trans2:exp7 0.0525268781011385 0.073436403582585 0.715270295638428 0.474675531752839 df.mm.trans1:exp8 -0.214179770109292 0.0872088262136492 -2.45594143859456 0.0142884266044614 * df.mm.trans2:exp8 -0.0922812428727185 0.073436403582585 -1.25661440880531 0.209303674169617 df.mm.trans1:probe2 -0.0376147222615569 0.0477662413307635 -0.78747502867326 0.431264800968308 df.mm.trans1:probe3 -0.062731106711059 0.0477662413307635 -1.31329376068486 0.189505268976121 df.mm.trans1:probe4 0.00975992069660006 0.0477662413307635 0.204326746771140 0.838156315747135 df.mm.trans1:probe5 -0.0657215647126921 0.0477662413307635 -1.37589985901538 0.169283455349902 df.mm.trans1:probe6 0.132761936918173 0.0477662413307635 2.77940933218601 0.0055888315320833 ** df.mm.trans1:probe7 0.0627662543938703 0.0477662413307635 1.31402958753311 0.189257664004006 df.mm.trans1:probe8 0.0681971511249818 0.0477662413307635 1.4277269725441 0.153807066048547 df.mm.trans1:probe9 0.368476101489459 0.0477662413307635 7.71415315971584 4.09712934056421e-14 *** df.mm.trans1:probe10 0.140068018949659 0.0477662413307635 2.93236426077027 0.00347140124344364 ** df.mm.trans1:probe11 0.708771451645171 0.0477662413307635 14.8383341853756 1.27867754514833e-43 *** df.mm.trans1:probe12 0.35032377976443 0.0477662413307635 7.33412908373024 6.063732749683e-13 *** df.mm.trans1:probe13 0.291701340093337 0.0477662413307635 6.10685144919428 1.66726882549343e-09 *** df.mm.trans1:probe14 0.250620484767541 0.0477662413307635 5.246811927949 2.04143920793134e-07 *** df.mm.trans1:probe15 0.188012174240616 0.0477662413307635 3.9360889406956 9.09123733073638e-05 *** df.mm.trans1:probe16 0.204300150521774 0.0477662413307635 4.27708240862143 2.15096261238776e-05 *** df.mm.trans1:probe17 0.111670124037951 0.0477662413307635 2.33784616345835 0.019670111470322 * df.mm.trans1:probe18 -0.0490502893319849 0.0477662413307635 -1.02688191420233 0.304823339247254 df.mm.trans1:probe19 0.0913536640765088 0.0477662413307635 1.91251523107959 0.0562094058906168 . df.mm.trans1:probe20 0.116993946313471 0.0477662413307635 2.44930191394654 0.0145522763141680 * df.mm.trans1:probe21 0.050434920484703 0.0477662413307635 1.05586956560931 0.291384442185258 df.mm.trans1:probe22 -0.0292834068401297 0.0477662413307635 -0.61305654420981 0.540033903081934 df.mm.trans2:probe2 -0.0558232312001313 0.0477662413307635 -1.16867540013408 0.242923932874875 df.mm.trans2:probe3 0.291560717341196 0.0477662413307635 6.10390747143461 1.69673484593725e-09 *** df.mm.trans2:probe4 -0.104792079657077 0.0477662413307635 -2.19385232619479 0.02856715937818 * df.mm.trans2:probe5 -0.108759103749596 0.0477662413307635 -2.27690311650188 0.0230866390992071 * df.mm.trans2:probe6 -0.0249499242611013 0.0477662413307635 -0.522333840092888 0.601599701967467 df.mm.trans3:probe2 -0.0720421008070654 0.0477662413307635 -1.50822209995969 0.131939410662954 df.mm.trans3:probe3 0.484687833280214 0.0477662413307635 10.1470791876616 1.07804684021658e-22 *** df.mm.trans3:probe4 0.307254315657281 0.0477662413307635 6.4324574657164 2.29767450659303e-10 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.82555224980262 0.133924586751715 28.5649733375312 2.51664893719046e-120 *** df.mm.trans1 0.0561956856498513 0.11892762491635 0.472520036361423 0.636699836282962 df.mm.trans2 0.280513138183688 0.108180140262536 2.59301880643645 0.00970828706212993 ** df.mm.exp2 0.190703949176828 0.145842904956153 1.30759840003298 0.191429848385724 df.mm.exp3 0.2650355765962 0.145842904956153 1.81726753643505 0.069594624522456 . df.mm.exp4 -0.02349165272055 0.145842904956153 -0.161075046657996 0.872079793861508 df.mm.exp5 0.205549522185460 0.145842904956153 1.40938993396529 0.159154678131234 df.mm.exp6 0.097273958049865 0.145842904956153 0.66697765022652 0.505001588413033 df.mm.exp7 0.307263983819774 0.145842904956153 2.106814753259 0.0354814228276143 * df.mm.exp8 0.194454254197394 0.145842904956153 1.33331308955931 0.182853710248011 df.mm.trans1:exp2 -0.190415306905051 0.138484680512330 -1.37499184892222 0.16956466961239 df.mm.trans2:exp2 -0.185327116119279 0.116614536964346 -1.58922824669742 0.112450926041822 df.mm.trans1:exp3 -0.258405901002619 0.13848468051233 -1.86595297073030 0.0624571929821445 . df.mm.trans2:exp3 -0.238808898117566 0.116614536964346 -2.04784844440604 0.0409396563638405 * df.mm.trans1:exp4 0.0310141401662031 0.138484680512330 0.223953581374235 0.822857398285864 df.mm.trans2:exp4 -0.12246687108176 0.116614536964346 -1.05018528795603 0.293987754169491 df.mm.trans1:exp5 -0.173742087058699 0.138484680512330 -1.25459427292559 0.210036035880603 df.mm.trans2:exp5 -0.191790107367679 0.116614536964346 -1.64465007845736 0.100481542486184 df.mm.trans1:exp6 -0.115122106873174 0.13848468051233 -0.831298497763617 0.406082544289646 df.mm.trans2:exp6 -0.152140917867724 0.116614536964346 -1.30464796095053 0.192432510709282 df.mm.trans1:exp7 -0.268680432490528 0.138484680512330 -1.94014552004260 0.0527548539287364 . df.mm.trans2:exp7 -0.247128568702437 0.116614536964346 -2.11919178462283 0.0344183531792903 * df.mm.trans1:exp8 -0.191839886937071 0.138484680512330 -1.38527876316248 0.166399252014542 df.mm.trans2:exp8 -0.110246970377820 0.116614536964346 -0.94539645954713 0.344775875724436 df.mm.trans1:probe2 0.0417192534079819 0.0758511833854991 0.550014535646092 0.582481171739627 df.mm.trans1:probe3 0.139778950989060 0.0758511833854991 1.84280514489352 0.0657712582962087 . df.mm.trans1:probe4 0.153321474722745 0.0758511833854991 2.02134584958969 0.0436160823765336 * df.mm.trans1:probe5 0.111544279309721 0.0758511833854991 1.47056742335606 0.141848058879769 df.mm.trans1:probe6 0.0431595212407001 0.0758511833854991 0.569002608981723 0.569533056782399 df.mm.trans1:probe7 0.144739276257522 0.0758511833854991 1.90820063441743 0.0567655246902355 . df.mm.trans1:probe8 0.069589505204877 0.0758511833854991 0.917447851158784 0.359217364099913 df.mm.trans1:probe9 0.0293864658579661 0.0758511833854991 0.387422641893601 0.69855864029613 df.mm.trans1:probe10 0.0787507505134684 0.0758511833854991 1.03822705195294 0.299515333026529 df.mm.trans1:probe11 0.0335904145032178 0.0758511833854991 0.442846281415293 0.658010869156223 df.mm.trans1:probe12 0.0807042679512692 0.0758511833854991 1.06398165920636 0.287696199863433 df.mm.trans1:probe13 0.0825267589497324 0.0758511833854991 1.08800885188970 0.276957807545811 df.mm.trans1:probe14 0.0571996311751898 0.0758511833854991 0.75410334581708 0.451035441516505 df.mm.trans1:probe15 0.0984139182631704 0.0758511833854991 1.29746055197320 0.194891223877681 df.mm.trans1:probe16 0.0778301601822011 0.0758511833854991 1.02609025605631 0.305196050254668 df.mm.trans1:probe17 0.187924563593772 0.0758511833854991 2.47754293612905 0.0134590272444535 * df.mm.trans1:probe18 0.164886710428938 0.0758511833854991 2.17381856247295 0.0300461582486911 * df.mm.trans1:probe19 0.133740933997582 0.0758511833854991 1.76320168029376 0.0782935892017843 . df.mm.trans1:probe20 0.120064710488932 0.0758511833854991 1.58289831654604 0.113886843199235 df.mm.trans1:probe21 0.0473565375858116 0.0758511833854991 0.624334855069183 0.532606853841824 df.mm.trans1:probe22 0.0546906575843891 0.0758511833854991 0.721025765760757 0.471129343859016 df.mm.trans2:probe2 -0.114712295668323 0.0758511833854991 -1.512333632098 0.130890860133657 df.mm.trans2:probe3 -0.0739261888381626 0.0758511833854991 -0.974621430260974 0.330077692764650 df.mm.trans2:probe4 -0.114883501275448 0.0758511833854991 -1.51459075716162 0.130317992939337 df.mm.trans2:probe5 -0.0693350539363701 0.0758511833854991 -0.914093239442131 0.360975996942730 df.mm.trans2:probe6 -0.0920201739338454 0.0758511833854991 -1.21316728133522 0.225466505118278 df.mm.trans3:probe2 -0.00652094193569312 0.0758511833854991 -0.0859702069847966 0.931514168825634 df.mm.trans3:probe3 -0.0345234642130129 0.0758511833854991 -0.455147338144403 0.649141414464345 df.mm.trans3:probe4 -0.0351318163567945 0.0758511833854991 -0.463167676346508 0.643385126630501